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We propose two continuous-time neural networks for computing generalized inverses of complex-valued matrices with rank-deficient cases. The first of them is applicable in the pseudoinverse computation and the second one is applicable in construction of outer inverses. The proposed continuous-time neural networks have a low complexity of implementation and they are proved to be globally convergent...
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